IGenBench：文本到信息图生成可靠性基准测试
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation
AI总结：
提出IGENBENCH基准，包含30种信息图类型和600个测试用例，通过多模态大语言模型分解为10类原子问题评估10种T2I模型，发现数据完整性等维度是普遍瓶颈。
摘要：
信息图是结合数据可视化与文本和插图元素的复合视觉制品，用于传达信息。虽然最近的文本到图像（T2I）模型可以生成美观的图像，但它们在生成信息图方面的可靠性仍不清楚。生成的信息图可能乍看正确，但包含容易被忽视的问题，例如扭曲的数据编码或错误的文本内容。我们提出了IGENBENCH，这是第一个评估文本到信息图生成可靠性的基准，包含跨越30种信息图类型的600个精心设计的测试用例。我们设计了一个自动评估框架，将可靠性验证分解为基于10种问题类型的原子是否问题。我们使用多模态大语言模型（MLLM）验证每个问题，得到问题级准确率（Q-ACC）和信息图级准确率（I-ACC）。我们在IGENBENCH上全面评估了10个最先进的T2I模型。我们的系统分析揭示了未来模型开发的关键见解：（i）三级性能层次，顶级模型的Q-ACC为0.90，但I-ACC仅为0.49；（ii）数据相关维度成为普遍瓶颈（例如，数据完整性：0.21）；（iii）所有模型实现端到端正确性的挑战。我们在https://this URL发布IGENBENCH。
英文摘要：
Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information. While recent text-to-image (T2I) models can generate aesthetically appealing images, their reliability in generating infographics remains unclear. Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content. We present IGENBENCH, the first benchmark for evaluating the reliability of text-to-infographic generation, comprising 600 curated test cases spanning 30 infographic types. We design an automated evaluation framework that decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types. We employ multimodal large language models (MLLMs) to verify each question, yielding question-level accuracy (Q-ACC) and infographic-level accuracy (I-ACC). We comprehensively evaluate 10 state-of-the-art T2I models on IGENBENCH. Our systematic analysis reveals key insights for future model development: (i) a three-tier performance hierarchy with the top model achieving Q-ACC of 0.90 but I-ACC of only 0.49; (ii) data-related dimensions emerging as universal bottlenecks (e.g., Data Completeness: 0.21); and (iii) the challenge of achieving end-to-end correctness across all models. We release IGENBENCH at https://igen-bench.vercel.app/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
